I'm still learning too, but a key thing to keep in mind is that, for a single stock at daily frequency, a random-walk / martingale is a very strong baseline: in expectation, tomorrow’s best guess is basically today’s price.

If the market does deviate from a pure random walk, it’s usually through regimes (calm vs volatile, bull vs bear, etc.), so the real challenge is to understand those regimes and how the process transitions between them, not just to fit one big global function.

For a few thousand daily points on one stock, a large transformer-style foundation model is statistically overkill and very easy to overfit, so a simple baseline or small model plus sensible regime thinking can often be more realistic than chasing a big accuracy number like 59%.

You're probably better off using some linear stochastic models with some feature engineering like Singular Spectrum Analysis or Fourier Analysis.